code: def forward(
      self, model: FairseqEncoderModel, sample, reduction="mean"
  ):
      src_tokens = sample["net_input"]["src_tokens"]
      src_lens = sample["net_input"]["src_lengths"]
      tgt_lens = sample["target_lengths"]
      (
          _feat_out,
          _feat_out_post,
          _,
          log_dur_out,
          pitch_out,
          energy_out,
      ) = model(
          src_tokens=src_tokens,
          src_lengths=src_lens,
          prev_output_tokens=sample["net_input"]["prev_output_tokens"],
          incremental_state=None,
          target_lengths=tgt_lens,
          speaker=sample["speaker"],
          durations=sample["durations"],
          pitches=sample["pitches"],
          energies=sample["energies"],
      )
  
      src_mask = lengths_to_mask(sample["net_input"]["src_lengths"])
      tgt_mask = lengths_to_mask(sample["target_lengths"])
  
      pitches, energies = sample["pitches"], sample["energies"]
      pitch_out, pitches = pitch_out[src_mask], pitches[src_mask]
      energy_out, energies = energy_out[src_mask], energies[src_mask]
  
      feat_out, feat = _feat_out[tgt_mask], sample["target"][tgt_mask]
      l1_loss = F.l1_loss(feat_out, feat, reduction=reduction)
      if _feat_out_post is not None:
          l1_loss += F.l1_loss(
              _feat_out_post[tgt_mask], feat, reduction=reduction
          )
  
      pitch_loss = F.mse_loss(pitch_out, pitches, reduction=reduction)
      energy_loss = F.mse_loss(
          energy_out, energies, reduction=reduction
      )
  
      log_dur_out = log_dur_out[src_mask]
      dur = sample["durations"].float()
      dur = (
          dur.half()
          if log_dur_out.type().endswith(".HalfTensor")
          else dur
      )
      log_dur = torch.log(dur + 1)[src_mask]
      dur_loss = F.mse_loss(log_dur_out, log_dur, reduction=reduction)
  
      ctc_loss = torch.tensor(0.0).type_as(l1_loss)
      if self.ctc_weight > 0.0:
          lprobs = model.get_normalized_probs(
              (_feat_out,), log_probs=True
          )
          lprobs = lprobs.transpose(0, 1)  # T x B x C
          src_mask = lengths_to_mask(src_lens)
          src_tokens_flat = src_tokens.masked_select(src_mask)
          ctc_loss = (
              F.ctc_loss(
                  lprobs,
                  src_tokens_flat,
                  tgt_lens,
                  src_lens,
                  reduction=reduction,
                  zero_infinity=True,
              )
              * self.ctc_weight
          )
  
      loss = l1_loss + dur_loss + pitch_loss + energy_loss + ctc_loss
  
      sample_size = sample["nsentences"]
      logging_output = {
          "loss": utils.item(loss.data),
          "ntokens": sample["ntokens"],
          "nsentences": sample["nsentences"],
          "sample_size": sample_size,
          "l1_loss": utils.item(l1_loss.data),
          "dur_loss": utils.item(dur_loss.data),
          "pitch_loss": utils.item(pitch_loss.data),
          "energy_loss": utils.item(energy_loss.data),
          "ctc_loss": utils.item(ctc_loss.data),
      }
      return loss, sample_size, logging_output
  
file_path: fairseq/fairseq/criterions/fastspeech2_loss.py
imports: from typing import List, Dict, Any
  from dataclasses import dataclass, field
  import torch
  import torch.nn.functional
  from fairseq import utils
  from fairseq.logging import metrics
  from fairseq.criterions import FairseqCriterion, register_criterion
  from fairseq.dataclass import FairseqDataclass
  from fairseq.data.data_utils import lengths_to_mask
  from fairseq.models.fairseq_model import FairseqEncoderModel
label_desc: Use image node_id 2 for calling the FastSpeech2Loss obj's underlying member method code with example usage: obj.forward(model, sample, reduction) and returns: loss,  sample_size,  logging_output